# Generative-Adversarial Networks for Low-Resource Language Data Augmentation in Machine Translation

arXiv: https://arxiv.org/abs/2409.00071 · HTML: https://mlsanity.com/paper/2409.00071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [A Theoretical Analysis of the Repetition Problem in Text Generation](https://mlsanity.com/paper/2012.14660/) (2020)
- [Universal Neural Machine Translation for Extremely Low Resource Languages](https://mlsanity.com/paper/1802.05368/) (2018)
- [Neural Machine Translation with Monolingual Translation Memory](https://mlsanity.com/paper/2105.11269/) (2021)
- [Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets](https://mlsanity.com/paper/1703.04887/) (2017)
- [Unsupervised Neural Machine Translation with Weight Sharing](https://mlsanity.com/paper/1804.09057/) (2018)
- [Generating Text through Adversarial Training using Skip-Thought Vectors](https://mlsanity.com/paper/1808.08703/) (2018)
- [Bilingual-GAN: A Step Towards Parallel Text Generation](https://mlsanity.com/paper/1904.04742/) (2019)
- [EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT](https://mlsanity.com/paper/2109.14368/) (2021)
- [Machine Translation of Low-Resource Indo-European Languages](https://mlsanity.com/paper/2108.03739/) (2021)
- [Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning](https://mlsanity.com/paper/2303.18011/) (2023)

## Abstract (arXiv)

Neural Machine Translation (NMT) systems struggle when translating to and from low-resource languages, which lack large-scale data corpora for models to use for training. As manual data curation is expensive and time-consuming, we propose utilizing a generative-adversarial network (GAN) to augment low-resource language data. When training on a very small amount of language data (under 20,000 sentences) in a simulated low-resource setting, our model shows potential at data augmentation, generating monolingual language data with sentences such as "ask me that healthy lunch im cooking up," and "my grandfather work harder than your grandfather before." Our novel data augmentation approach takes the first step in investigating the capability of GANs in low-resource NMT, and our results suggest that there is promise for future extension of GANs to low-resource NMT.